class10
Dominique Lie (A15470100)
10/28/2021
1. Importing candy data
candy_file <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/candy-power-ranking/candy-data.csv"
candy = read.csv(candy_file, row.names = 1)
head(candy)## chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand 1 0 1 0 0 1
## 3 Musketeers 1 0 0 0 1 0
## One dime 0 0 0 0 0 0
## One quarter 0 0 0 0 0 0
## Air Heads 0 1 0 0 0 0
## Almond Joy 1 0 0 1 0 0
## hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand 0 1 0 0.732 0.860 66.97173
## 3 Musketeers 0 1 0 0.604 0.511 67.60294
## One dime 0 0 0 0.011 0.116 32.26109
## One quarter 0 0 0 0.011 0.511 46.11650
## Air Heads 0 0 0 0.906 0.511 52.34146
## Almond Joy 0 1 0 0.465 0.767 50.34755Q1. How many different candy types in this dataset?
nrow(candy)## [1] 85Q2. How many fruity candy types in the dataset?
sum(candy$fruity)## [1] 382. What is your favorite candy?
candy["Twix", ]$winpercent## [1] 81.64291Q3. What is your favorite candy in the dataset and what is it’s winpercent value?
candy["Red vines", ]$winpercent## [1] 37.34852Q4. What is the winpercent value for “Kit Kat”?
candy["Kit Kat", ]$winpercent## [1] 76.7686Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?
candy["Tootsie Roll Snack Bars", ]$winpercent## [1] 49.6535# install.packages("skimr")
library("skimr")
skim(candy)| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?
winpercent - on percentage scale to 100 rather than to 1
Q7. What do you think a zero and one represent for the candy$chocolate column?
A zero means that it is not chocolate and one means that it is chocolate.
Q8. Plot histogram of winpercent values
hist(candy$winpercent)
> Q9. Is the distribution of winpercent values symmetrical?
No, it is slightly skewed to the left.
Q10. Is the center of the distribution above or below 50%?
The distribution is below 50%.
Q11. On average is chocolate candy higher or lower ranked than fruit candy?
choc <- candy$winpercent[as.logical(candy$chocolate)]
mean(choc)## [1] 60.92153fruit <- candy$winpercent[as.logical(candy$fruity)]
mean(fruit)## [1] 44.11974On average, the chocolate candy is higher ranked than fruit candy.
Q12. Is this different statistically significant?
t.test(choc, fruit)##
## Welch Two Sample t-test
##
## data: choc and fruit
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.44563 22.15795
## sample estimates:
## mean of x mean of y
## 60.92153 44.11974The difference is signficantly significant as the p-value is less than 0.05.
3. Overall Candy Rankings
Q13. What are the five least liked candy types in this set?
head(candy[order(candy$winpercent),], n = 5)## chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip 0 1 0 0 0
## Boston Baked Beans 0 0 0 1 0
## Chiclets 0 1 0 0 0
## Super Bubble 0 1 0 0 0
## Jawbusters 0 1 0 0 0
## crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip 0 0 0 1 0.197 0.976
## Boston Baked Beans 0 0 0 1 0.313 0.511
## Chiclets 0 0 0 1 0.046 0.325
## Super Bubble 0 0 0 0 0.162 0.116
## Jawbusters 0 1 0 1 0.093 0.511
## winpercent
## Nik L Nip 22.44534
## Boston Baked Beans 23.41782
## Chiclets 24.52499
## Super Bubble 27.30386
## Jawbusters 28.12744Q14. What are the top 5 all time favorite candy types out of this set?
head(candy[order(candy$winpercent, decreasing = TRUE), ], n = 5)## chocolate fruity caramel peanutyalmondy nougat
## ReeseÕs Peanut Butter cup 1 0 0 1 0
## ReeseÕs Miniatures 1 0 0 1 0
## Twix 1 0 1 0 0
## Kit Kat 1 0 0 0 0
## Snickers 1 0 1 1 1
## crispedricewafer hard bar pluribus sugarpercent
## ReeseÕs Peanut Butter cup 0 0 0 0 0.720
## ReeseÕs Miniatures 0 0 0 0 0.034
## Twix 1 0 1 0 0.546
## Kit Kat 1 0 1 0 0.313
## Snickers 0 0 1 0 0.546
## pricepercent winpercent
## ReeseÕs Peanut Butter cup 0.651 84.18029
## ReeseÕs Miniatures 0.279 81.86626
## Twix 0.906 81.64291
## Kit Kat 0.511 76.76860
## Snickers 0.651 76.67378Q15. Make a first barplot of candy ranking based on winpercent values.
library(ggplot2)
ggplot(candy) + aes(winpercent, rownames(candy)) + geom_bar(stat = "identity")
Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?
ggplot(candy) + aes(winpercent, reorder(rownames(candy), winpercent)) + geom_bar(stat = "identity")
Set up color
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols) 
# Taking a look at pricepercent
library(ggrepel)
# How about a plot of price vs win
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3, max.overlaps = 5)## Warning: ggrepel: 50 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?
Reese’s O Miniatures
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )## pricepercent winpercent
## Nik L Nip 0.976 22.44534
## Nestle Smarties 0.976 37.88719
## Ring pop 0.965 35.29076
## HersheyÕs Krackel 0.918 62.28448
## HersheyÕs Milk Chocolate 0.918 56.49050Q21. Make a barplot again with geom_col() this time using pricepercent and then improve this step by step, first ordering the x-axis by value and finally making a so called “dot chat” or “lollipop” chart by swapping geom_col() for geom_point() + geom_segment().
ggplot(candy) + aes(pricepercent, reorder(rownames(candy), pricepercent)) + geom_col()
ggplot(candy) +
aes(pricepercent, reorder(rownames(candy), pricepercent)) +
geom_segment(aes(yend = reorder(rownames(candy), pricepercent),
xend = 0), col="gray40") +
geom_point()
# 5. Exploring correlation structure
library(corrplot)## corrplot 0.90 loadedcij <- cor(candy)
corrplot(cij)
> Q22. Examining this plot what two variables are anti-correlated (i.e. have minus values)?
fruity and chocolate > Q23. Similarly, what two variables are most positively correlated?
winpercent and chocolate
6 Principal Component Analysis
pca <- prcomp(candy, scale = TRUE)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000plot(pca$x[,1:2])
plot(pca$x[,1:2], col=my_cols, pch=16)
# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])
p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols)
p
library(ggrepel)
p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")## Warning: ggrepel: 39 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(p)par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")
> Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you?
fruity, hard, and pluribus yes,it makes sense because a lot of candies that are fruity are hard and come in a package of more than one